This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more.
Apply Now
Role Description
We're seeking a data-driven analyst to conduct comprehensive failure analysis on AI agent performance across finance-sector tasks. You'll identify patterns, root causes, and systemic issues in our evaluation framework by analyzing task performance across multiple dimensions (task types, file types, criteria, etc.).
- Statistical Failure Analysis : Identify patterns in AI agent failures across task components (prompts, rubrics, templates, file types, tags)
- Root Cause Analysis : Determine whether failures stem from task design, rubric clarity, file complexity, or agent limitations
- Dimension Analysis : Analyze performance variations across finance sub-domains, file types, and task categories
- Reporting & Visualization : Create dashboards and reports highlighting failure clusters, edge cases, and improvement opportunities
- Quality Framework : Recommend improvements to task design, rubric structure, and evaluation criteria based on statistical findings
- Stakeholder Communication : Present insights to data labeling experts and technical teams
Qualifications
- Statistical Expertise : Strong foundation in statistical analysis, hypothesis testing, and pattern recognition
- Programming : Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis
- Data Analysis : Experience with exploratory data analysis and creating actionable insights from complex datasets
- AI/ML Familiarity : Understanding of LLM evaluation methods and quality metrics
- Tools : Comfortable working with Excel, data visualization tools (Tableau/Looker), and SQL
Requirements
- Experience with AI/ML model evaluation or quality assurance
- Background in finance or willingness to learn finance domain concepts
- Experience with multi-dimensional failure analysis
- Familiarity with benchmark datasets and evaluation frameworks
- 2-4 years of relevant experience
Apply Now